Surveillance cameras are used at a multitude of locations and are used for many purposes including security and activity tracking. In certain applications, human viewing of the footage may be feasible for the purpose of the system, e.g. looking back at footage for a suspected thief in a grocery store. For other applications however, human viewing is not feasible at scale, e.g. for tracking the number of people in an area 24/7. For these applications, an automated system that tracks the number of people and their locations in an image is of use.
In this project, I will attempt to design an algorithm that views the footage of surveillance cameras and determines 1) the number of people in the image 2) where in the view of the camera they are located. I will design this algorithm using probabilistic modeling and Markov Chain Monte Carlo (MCMC).
This problem is contained within computer vision, which is a broad field of computer science and applied mathematics. Within this field, classification of images are made with a variety of tools including neural networks, image segmentation algorithms, and MCMC. Specific to surveillance camera footage, there has been a multitude project made. Examples include http://wearables.cc.gatech.edu/paper_of_week/RealTimeTracking-Stauffer,Grimson.pdf tracks object modelling pixels as Gaussian mixtures, whereas https://www.cc.gatech.edu/~dellaert/pubs/Khan05pami.pdf uses particle filtering to track movements of objects, whereas http://www.cse.psu.edu/~rtc12/Papers/LiuCollinsLiuBMVC2011.pdf study the auxillary problem of camera auto calibration and http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0FDDF7D82220DB0C7966BC8D007AA447?doi=10.1.1.53.3730&rep=rep1&type=pdf background and foreground estimation. The closest work to this project is http://www.cse.psu.edu/~rtc12/CSE586/papers/mcmcCvpr03Zhao.pdf and http://vision.cse.psu.edu/publications/pdfs/2009ge3.pdf, both performing the estimation by Reversible Jump Monte Carlo Markov Chains (RJMCMC), with different approaches to modelling.
In order to make the scope of the project feasible, I decided to go with a surveillance footage data set that was relatively easy. Factors that make the problem of couting and locating humans in surveillance footage hard include but are not limited to: * Views where the background, lighting and scene changes, such as outside * Dense crowds, where people are occluding each other * People with different postures and orientations, such as some sitting while others standing * Areas in the background that are hard to distinguish from the foreground, e.g. glass or mirrors that reflect people in the scene * Obscure camera angels with perspective distortion effects making modelling harder
Following are some examples of data sets of surveillance footage that I reviewed:
Mall dataset https://personal.ie.cuhk.edu.hk/~ccloy/downloads_mall_dataset.html. This dataset is relatively hard because of 1) the glass on the store windows reflecting people in the scene crowded scene, the palm tree occluding people behind it, people sitting on the bench below the palm tree, people carrying e.g. bags and strollers.
TODO
TODO
I decided to go with http://groups.inf.ed.ac.uk/vision/CAVIAR/CAVIARDATA1/ as a starting point since it was less complex than other datasets.
For my approach, first step in attempting to solve the problem is separating the foreground and background from each other, so that the foreground can be searched for humans. http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=0FDDF7D82220DB0C7966BC8D007AA447?doi=10.1.1.53.3730&rep=rep1&type=pdf suggests a Kalman filter approach for this leading to good results, while http://www.cse.psu.edu/~rtc12/CSE586/papers/mcmcCvpr03Zhao.pdf and http://vision.cse.psu.edu/publications/pdfs/2009ge3.pdf use standard techniques for background subtraction. The closest to my approach is http://wearables.cc.gatech.edu/paper_of_week/RealTimeTracking-Stauffer,Grimson.pdf which models each pixel as a gaussian mixture, and labels a pixel as foreground if it is sufficiently unlikely given this model.
First, I estimated the background image using the mean of the color values in each pixel from a portion of the data set. The intuition behind this approach is that foreground pixels get “smudged out” when averaged, since most frames have most of the background visible and objects are in different positions across frames. The mean image of 3000 randomly sampled images from the data set may be seen in fig X.
Backgound estimating by computing the mean image
In order to classify the foreground of a given frame using this background image, I computed the difference between the mean of the color values in each pixel (effectively converting it to gray scale) of the frame and background image. If this difference was over a certain threshold, it is considered foreground, otherwise background. This leads to a foreground mask as seen in fig X.
Example frame Foreground mask for the example frame, comparing it to the background image with a fixed threshold
One issue with this approach is that the variance is different among pixels, as some are more affected e.g. by natural variations such as lighting. A problem with this approach may be seen in figure (above), the floor pixels in top-middle of the image are classified as background since the lighting is different in those pixels from the frame and the mean image. This implies that a per-pixel threshold would increase performance. In order to do this, I calculate the mean square error of a random sample of frames compared to the background, effectively estimating the variance of the pixels. A multiple of the square root of the variance is then used as a threshold. This approach can effectively be viewed as modeling each pixel as a Gaussian with a certain mean and variance for being a background pixel, and classifying pixels as foreground if they are sufficiently unlikely given this description. The result of this method may be seen in fig X. There are still some artifacts from the shop window, and a person “blending” in to the background as their clothing is too similar to the background, but result is considered sufficiently good for the next step.
Foreground mask for the example frame, using a per-pixel threshold Square root of the variance map produced in order to determine the per-pixel threshold
Other approaches would be a smooth segmentation of foreground and background pixels, where e.g. edges of shapes are less defined. For this purpose, http://vision.cse.psu.edu/publications/pdfs/2009ge3.pdf models pixels as Bernoulli distributions. One way to reduce the frequency of people blending in to the background is comparing the images in color rather than in gray scale, as this would help classification in cases where the person has clothing that has the same gray scale but not color value as the background, taking advantage of more information in the classifications.
The problem is modeled using Bayesian probabilities:
\[ P(\theta|I)=\frac{P(I|\theta)P(\theta)}{P(I)}\propto P(I|\theta)P(\theta) \]
where \(\theta\) are the parameters and \(I\) the observed image. As such, \(P(\theta|I)\) is the posterior distribution \(\pi\), \(P(I|\theta)\) the likelihood and \(P(\theta)\) the prior. \(P(I)\) can be viewed as normalizing constant. This constant being hard to determine motivates the use of MCMC for sampling \(\pi\).
The solution to the problem is defined as the maximum a priori (MAP) estimation \(\theta^*\) that maximizes \(\pi\).
As the goal of a given frame is to determine the amount of people in the image and their location, the solution space \(\Theta\) has different dimensions depending on the amount of people in the frame. The solutions space is of the form \((k,\)
Camera geometry extension ## Likelihood # Simulation ## Proposals Data driven ## MCMC setup # Result Mall dataset ## Accuracy # Discussion ## Model diagnostics / assumptions ## MCMC diagnostics ## Conclusion ## Extentions and next steps ### Particle filtering / between frames Multiple frames
library(jpeg)
image_files <- Sys.glob("./CAVIAR/**/**.jpg")
sample_length <- 3000
sample_frames <- sample(1:length(image_files), size = sample_length, replace = F)
w <- 384
h <- 288
plot_image <- function(image, title = NULL) {
plot(0:1,0:1,type="n",ann=!is.null(title),axes=FALSE, main=title)
rasterImage(image,0,0,1,1)
}
to_grayscale <- function(image) {
gray <- array(0, dim=c(h,w))
for (x in 1:w) {
for (y in 1:h) {
gray[y, x] = mean(image[y, x, c(1,2,3)])
}
}
return (gray)
}
from_grayscale <- function(gray) {
image <- array(0, dim=c(h,w,3))
for (x in 1:w) {
for (y in 1:h) {
image[y, x, c(1,2,3)] = gray[y, x]
}
}
return (image)
}
background <- array(0, dim=c(h,w,3))
for (file in image_files[sample_frames]) {
frame <- readJPEG(file, native=FALSE)
background <- background + frame / sample_length
}
clamp <- function(x, minv, maxv) {
return (min(max(x, minv), maxv))
}
for (i in 1:h) {
for (j in 1:w) {
for (c in 1:3) {
background[i, j, c] <- clamp(background[i, j, c], 0, 1)
}
}
}
background_grayscale <- to_grayscale(background)
plot_image(background)
plot_image(from_grayscale(background_grayscale))
writeJPEG(background, "bg.jpg")
writeJPEG(from_grayscale(background_grayscale), "bg_gray.jpg")
sample_length <- 100
sample_frames <- sample(1:length(image_files), size = sample_length, replace = F)
background_var <- array(0, dim=c(h,w))
for (file in image_files[sample_frames]) {
frame <- readJPEG(file, native=FALSE)
for (i in 1:h) {
for (j in 1:w) {
background_var[i, j] <- background_var[i, j] + (mean(frame[i, j, c(1,2,3)]) - background_grayscale[i, j])^2 / sample_length^2
}
}
}
background_sd <- sqrt(background_var)
hist(2 * background_sd)
plot_image(background)
plot_image(from_grayscale(background_sd * 10))
library(xml2)
folders <- Sys.glob("./CAVIAR/*")
xml_files <- Sys.glob("./CAVIAR/**/**.xml")
ks <- c()
obs <- data.frame()
for (f in xml_files) {
# f <- xml_files[1]
doc <- read_xml(f)
# obj_list <- xml_find_all(doc, "frame//objectlist")[1]
for (obj_list in xml_find_all(doc, "frame//objectlist")) {
boxes <- xml_find_all(obj_list, "object/box")
ks <- c(ks, length(boxes))
# obj_attrs <- xml_attrs(boxes)[[1]]
for (obj_attrs in xml_attrs(boxes)) {
# obs <- rbind(obs, x=as.numeric(obj_attrs$x))
obs <- rbind(obs, list(
x=as.numeric(obj_attrs["xc"]),
y=as.numeric(obj_attrs["yc"]),
w=as.numeric(obj_attrs["w"]),
h=as.numeric(obj_attrs["h"])))
}
}
}
# library(ggplot2)
library(GGally)
## Loading required package: ggplot2
## Registered S3 method overwritten by 'GGally':
## method from
## +.gg ggplot2
hist(ks)
ggpairs(obs)
plot_image(background)
points(obs$x/w, 1-obs$y/h)
summary(lm(w ~ y, data = obs))
##
## Call:
## lm(formula = w ~ y, data = obs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -56.179 -1.584 0.343 2.594 39.016
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 9.7304282 0.0727406 133.8 <2e-16 ***
## y 0.1731687 0.0005913 292.9 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.856 on 33226 degrees of freedom
## Multiple R-squared: 0.7208, Adjusted R-squared: 0.7208
## F-statistic: 8.577e+04 on 1 and 33226 DF, p-value: < 2.2e-16
h_cutoff <- 210
summary(lm(obs$h[which(obs$y <= h_cutoff)] ~ obs$y[which(obs$y <= h_cutoff)], data = obs))
##
## Call:
## lm(formula = obs$h[which(obs$y <= h_cutoff)] ~ obs$y[which(obs$y <=
## h_cutoff)], data = obs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -114.899 -2.890 -0.084 3.852 17.561
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.240e+01 8.083e-02 277.1 <2e-16 ***
## obs$y[which(obs$y <= h_cutoff)] 5.403e-01 9.122e-04 592.3 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7.225 on 28377 degrees of freedom
## Multiple R-squared: 0.9252, Adjusted R-squared: 0.9252
## F-statistic: 3.508e+05 on 1 and 28377 DF, p-value: < 2.2e-16
summary(lm(obs$h[which(obs$y > h_cutoff)] ~ obs$y[which(obs$y > h_cutoff)], data = obs))
##
## Call:
## lm(formula = obs$h[which(obs$y > h_cutoff)] ~ obs$y[which(obs$y >
## h_cutoff)], data = obs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -95.988 -0.271 0.387 0.812 3.800
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 564.416391 0.669232 843.4 <2e-16 ***
## obs$y[which(obs$y > h_cutoff)] -1.988206 0.002785 -714.0 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 3.673 on 4847 degrees of freedom
## Multiple R-squared: 0.9906, Adjusted R-squared: 0.9906
## F-statistic: 5.098e+05 on 1 and 4847 DF, p-value: < 2.2e-16
w_model <- function(y) {
11.6 + 0.14*y
}
h_model <- function(y) {
ifelse(y <= h_cutoff, 22.8 + 0.5 * y, 527.8 - 1.86 * y)
}
w_sd <- 7
h_sd <- 12
min_w <- 5
min_h <- 5
# current_frame_nr <- 2000 # många personer spridda
# current_frame_nr <- 3000 # många personer klumpade
# current_frame_nr <- 5000 # 4 personer spridda
# current_frame_nr <- 4000 # många personer långt borta klumpade
# current_frame_nr <- 200 # många personer spridda
# current_frame_nr <- 2500 # en nära, nåra borta
# current_frame_nr <- 800 # en nära, nåra borta
# current_frame_nr <- 4500 # tre personer, två ihop, en ensam nära
current_frame_nr <- 20; current_k <- 4 # två nära ihop, två borta ihop
# current_frame_nr <- 3100 # många ihop
# current_frame_nr <- 5600 # tre nära
# current_frame_nr <- 5950 # två nära en vid väggen
# current_frame_nr <- 5750; current_k <- 4#; current_k <- 5 # fem utspridda
# current_frame_nr <- 5964-47+2; current_k <- 3 # två spridda, en vid väggen
current_frame <- readJPEG(image_files[current_frame_nr], native=FALSE)
plot_image(current_frame)
plot_image(current_frame)
xs <- runif(50)
ys <- runif(50)
ws <- rnorm(50, mean = w_model(ys*h), sd = w_sd)/h
hs <- rnorm(50, mean = h_model(ys*h), sd = h_sd)/h
rect(xs - ws/2, 1-(ys - hs/2), xs + ws/2, 1-(ys + ys/2), col=rgb(1,0,0,0.1))
plot(obs$y, obs$w)
points(ys*h, ws*h, col="red")
plot(obs$y, obs$h)
points(ys*h, hs*h, col="red")
threshold <- 0.2
sd_multiplier <- 20
# threshold <- 0
# sd_multiplier <- 0
background <- readJPEG("./bg.jpg", native=FALSE)
background_subtraction <- function(img, background) {
mask <- array(dim=c(h,w))
for (x in 1:w) {
for (y in 1:h) {
# mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)])-mean(background[y, x, c(1,2,3)])) < threshold, 0, 1)
# mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)]-background[y, x, c(1,2,3)])) < threshold, 0, 1)
mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)]) - mean(background[y, x, c(1,2,3)])) < max(sd_multiplier*background_sd[y, x], threshold), 0, 1)
# mask[y, x] <- ifelse(abs(mean(img[y, x, c(1,2,3)]) - mean(background[y, x, c(1,2,3)])) < max(sd_multiplier^2*background_var[y, x], threshold), 0, 1)
}
}
return (mask)
}
foreground <- background_subtraction(current_frame, background)
plot_image(from_grayscale(foreground))
plot_image(current_frame)
plot_image(background)
points <- data.frame()
for (x in 1:w) {
for (y in 1:h) {
if (foreground[y, x] == 1) {
points <- rbind(points, list(x=x,y=y))
}
}
}
plot(points$x, h-points$y)
clusters <- kmeans(points, current_k)
points(clusters$centers[,1], h-clusters$centers[,2], col="green")
for (k in 1:current_k) {
xc <- clusters$centers[k, 1]
yc <- clusters$centers[k, 2]
ws <- rnorm(50, mean = w_model(yc), sd = w_sd)
hs <- rnorm(50, mean = h_model(yc), sd = h_sd)
rect(
xc-ws/2,
h-(yc-hs/2),
xc+ws/2,
h-(yc+hs/2),
col = rgb(1,0,1,0.01)
)
}
points <- list()
for (k in 1:current_k) {
xc <- clusters$centers[k, 1]
yc <- clusters$centers[k, 2]
ww <- rnorm(1, mean = w_model(yc), sd = w_sd)
hh <- rnorm(1, mean = h_model(yc), sd = h_sd)
points[[k]] <- list(x=xc,y=yc,w=ww,h=hh)
}
initial_state <- list(
k = current_k,
points = points
)
render_state <- function(state, title=NULL) {
plot_image(from_grayscale(foreground), title)
cols <- c(
rgb(1,0,0,0.5),
rgb(0,1,0,0.5),
rgb(0,0,1,0.5),
rgb(1,0,1,0.5),
rgb(1,1,0,0.5)
)
i <- 1
for (point in state$points) {
col <- cols[i %% length(cols) + 1]
i <- i + 1
rect((point$x - point$w/2) / w, 1-(point$y - point$h/2) / h, (point$x + point$w/2)/w, 1-(point$y + point$h/2)/h, col = col)
}
}
render_state(initial_state)
points_unif <- list()
for (k in 1:current_k) {
xc <- runif(1, 0, w)
yc <- runif(1, 0, h)
ww <- rnorm(1, mean = w_model(yc), sd = w_sd)
hh <- rnorm(1, mean = h_model(yc), sd = h_sd)
points_unif[[k]] <- list(x=xc,y=yc,w=ww,h=hh)
}
initial_state_unif <- list(
k = current_k,
points = points_unif
)
render_state(initial_state_unif)
metropolis_hastings <- function(q_sample, q_ratio, pi_ratio, N, initial_state) {
state <- initial_state
states <- list(state)
for (i in 1:N) {
proposed <- q_sample(state)
alpha <- q_ratio(state, proposed) * pi_ratio(proposed, state)
if (alpha >= 1 || runif(1) <= alpha) {
state <- proposed
}
states <- append(states, list(state))
}
return (states)
}
likelihood <- function(state) {
state_mask <- array(0, dim=c(h, w))
outside <- 0
overlap <- 0
for (i in 1:state$k) {
point <- state$points[[i]]
for (x in round(point$x - point$w/2):round(point$x + point$w/2)) {
for (y in round(point$y - point$h/2):round(point$y + point$h/2)) {
if (y >= 1 && y <= h && x >= 1 && x <= w) {
if (state_mask[y, x] == 0) {
state_mask[y, x] = 1
} else {
overlap <- overlap + 1
}
} else {
outside <- outside + 1
}
}
}
}
N10 <- sum(state_mask == 1 & foreground == 0)
N01 <- sum(state_mask == 0 & foreground == 1)
measures <- c(N10, N01, outside, overlap)
# weights <- c(0.0001, 0.0001, 0.0001, 0.00005)
weights <- c(0.0005, 0.01, 0.0001, 0.00001)*10
Z <- measures %*% weights
# print(c(measures, exp(-Z), prior(state)))
return (exp(-Z) * prior(state))
}
pi_ratio <- function(state_1, state_2) {
likelihood(state_1) / likelihood(state_2)
}
x_sd <- w*.03
y_sd <- h*.03
propose <- function(state) {
i <- sample(1:state$k, 1)
p <- state$points[[i]]
# if (runif(1) < 0.8) {
state$points[[i]]$x <- clamp(rnorm(1, mean = p$x, sd=x_sd), p$w/2, w-p$w/2)
state$points[[i]]$y <- clamp(rnorm(1, mean = p$y, sd=y_sd), p$h/2, h-p$h/2)
# } else {
p <- state$points[[i]]
state$points[[i]]$w <- clamp(rnorm(1, mean = w_model(p$y), sd = w_sd), min_w, w)
state$points[[i]]$h <- clamp(rnorm(1, mean = h_model(p$y), sd = h_sd), min_h, h)
# }
return (state)
}
prior <- function(x) {
prob <- 1
for (p in x$points) {
prob <- prob * dnorm(p$w, mean = w_model(p$y), sd = w_sd) * dnorm(p$h, mean = h_model(p$y), sd = h_sd)
}
prob
}
proposal_prob <- function(x) {
prior(x)
# prob <- 1
# # for (p in x$points) {
# # prob <- prob * dmvnorm(c(p$x, p$y), mean, sigma) * dmvnorm(c(p$w, p$h), mean_size, sigma_size)
# # }
# for (p in x$points) {
# prob <- prob * dmvnorm(c(p$w, p$h), mean_size, sigma_size)
# }
# prob
1
}
proposal_prob_ratio <- function(x, y) {
proposal_prob(x) / proposal_prob(y)
}
states <- metropolis_hastings(
propose,
proposal_prob_ratio,
pi_ratio,
1000,
initial_state
)
ls <- c()
ps <- c()
for (s in states) {
ls <- c(ls, likelihood(s))
ps <- c(ps, prior(s))
}
plot(ls, col="black", type="l", ylim=c(0,quantile(c(ls, ps), 0.8)))
# plot(ls, col="black", type="l")
lines(ps, col="red")
render_states <- function(states, indices, title) {
for (s in states[indices]) {
render_state(s, title)
}
}
render_states(states, seq(1,4), "first")
render_states(states, length(states)-seq(1,5)-1, "last")
render_states(states, order(ls, decreasing = 1)[c(seq(1,20))], "best")
render_states(states, order(ls, decreasing = 1)[c(length(states)-seq(1,5)-1)], "worst")
render_states(states, sample(1:length(states), 5), "random")
# s_good <- states[[1]]
# s_bad <- states[[order(ls, decreasing = 1)[1]]]
s_good <- states[[order(ls, decreasing = 1)[2]]]
s_bad <- states[[order(ls, decreasing = 1)[1]]]
render_state(s_good)
render_state(s_bad)
likelihood(s_good)
## [,1]
## [1,] 9.146149e-22
likelihood(s_bad)
## [,1]
## [1,] 9.146149e-22
locs <- data.frame()
for (s in states) {
locs <- rbind(locs, s$points[[1]])
}
locs
## x y w h
## 1 306.0924 20.15126 18.969385 8.746052
## 2 306.0924 20.15126 18.969385 8.746052
## 3 312.7157 17.02377 16.460052 27.692992
## 4 312.7157 17.02377 16.460052 27.692992
## 5 305.4751 15.09860 17.686801 28.478290
## 6 305.4751 15.09860 17.686801 28.478290
## 7 305.4751 15.09860 17.686801 28.478290
## 8 305.4751 15.09860 17.686801 28.478290
## 9 305.4751 15.09860 17.686801 28.478290
## 10 305.4751 15.09860 17.686801 28.478290
## 11 305.4751 15.09860 17.686801 28.478290
## 12 305.4751 15.09860 17.686801 28.478290
## 13 305.4751 15.09860 17.686801 28.478290
## 14 305.0750 18.14648 12.437057 24.861140
## 15 305.0750 18.14648 12.437057 24.861140
## 16 305.0750 18.14648 12.437057 24.861140
## 17 305.0750 18.14648 12.437057 24.861140
## 18 305.0750 18.14648 12.437057 24.861140
## 19 305.0750 18.14648 12.437057 24.861140
## 20 305.0750 18.14648 12.437057 24.861140
## 21 305.0750 18.14648 12.437057 24.861140
## 22 305.0750 18.14648 12.437057 24.861140
## 23 305.0750 18.14648 12.437057 24.861140
## 24 305.0750 18.14648 12.437057 24.861140
## 25 305.0750 18.14648 12.437057 24.861140
## 26 305.0750 18.14648 12.437057 24.861140
## 27 305.0750 18.14648 12.437057 24.861140
## 28 305.0750 18.14648 12.437057 24.861140
## 29 305.0750 18.14648 12.437057 24.861140
## 30 305.0750 18.14648 12.437057 24.861140
## 31 305.0750 18.14648 12.437057 24.861140
## 32 305.0750 18.14648 12.437057 24.861140
## 33 305.0750 18.14648 12.437057 24.861140
## 34 305.0750 18.14648 12.437057 24.861140
## 35 305.0750 18.14648 12.437057 24.861140
## 36 305.0750 18.14648 12.437057 24.861140
## 37 305.0750 18.14648 12.437057 24.861140
## 38 305.0750 18.14648 12.437057 24.861140
## 39 305.0750 18.14648 12.437057 24.861140
## 40 305.0750 18.14648 12.437057 24.861140
## 41 305.0750 18.14648 12.437057 24.861140
## 42 305.0750 18.14648 12.437057 24.861140
## 43 305.0750 18.14648 12.437057 24.861140
## 44 305.0750 18.14648 12.437057 24.861140
## 45 305.0750 18.14648 12.437057 24.861140
## 46 305.0750 18.14648 12.437057 24.861140
## 47 305.0750 18.14648 12.437057 24.861140
## 48 305.0750 18.14648 12.437057 24.861140
## 49 305.0750 18.14648 12.437057 24.861140
## 50 305.0750 18.14648 12.437057 24.861140
## 51 305.0750 18.14648 12.437057 24.861140
## 52 305.0750 18.14648 12.437057 24.861140
## 53 305.0750 18.14648 12.437057 24.861140
## 54 305.0750 18.14648 12.437057 24.861140
## 55 305.0750 18.14648 12.437057 24.861140
## 56 305.0750 18.14648 12.437057 24.861140
## 57 305.0750 18.14648 12.437057 24.861140
## 58 305.0750 18.14648 12.437057 24.861140
## 59 305.0750 18.14648 12.437057 24.861140
## 60 305.0750 18.14648 12.437057 24.861140
## 61 305.0750 18.14648 12.437057 24.861140
## 62 305.0750 18.14648 12.437057 24.861140
## 63 305.0750 18.14648 12.437057 24.861140
## 64 305.0750 18.14648 12.437057 24.861140
## 65 305.0750 18.14648 12.437057 24.861140
## 66 305.0750 18.14648 12.437057 24.861140
## 67 305.0750 18.14648 12.437057 24.861140
## 68 304.1862 23.01074 11.890172 25.702677
## 69 304.1862 23.01074 11.890172 25.702677
## 70 304.1862 23.01074 11.890172 25.702677
## 71 304.1862 23.01074 11.890172 25.702677
## 72 304.1862 23.01074 11.890172 25.702677
## 73 304.1862 23.01074 11.890172 25.702677
## 74 304.1862 23.01074 11.890172 25.702677
## 75 304.1862 23.01074 11.890172 25.702677
## 76 304.1862 23.01074 11.890172 25.702677
## 77 304.1862 23.01074 11.890172 25.702677
## 78 304.1862 23.01074 11.890172 25.702677
## 79 304.1862 23.01074 11.890172 25.702677
## 80 304.1862 23.01074 11.890172 25.702677
## 81 304.1862 23.01074 11.890172 25.702677
## 82 304.1862 23.01074 11.890172 25.702677
## 83 304.1862 23.01074 11.890172 25.702677
## 84 304.1862 23.01074 11.890172 25.702677
## 85 304.1862 23.01074 11.890172 25.702677
## 86 304.1862 23.01074 11.890172 25.702677
## 87 304.1862 23.01074 11.890172 25.702677
## 88 304.1862 23.01074 11.890172 25.702677
## 89 304.1862 23.01074 11.890172 25.702677
## 90 304.1862 23.01074 11.890172 25.702677
## 91 304.1862 23.01074 11.890172 25.702677
## 92 304.1862 23.01074 11.890172 25.702677
## 93 304.1862 23.01074 11.890172 25.702677
## 94 304.1862 23.01074 11.890172 25.702677
## 95 309.9951 17.26996 12.336746 23.735054
## 96 309.9951 17.26996 12.336746 23.735054
## 97 309.9951 17.26996 12.336746 23.735054
## 98 309.9951 17.26996 12.336746 23.735054
## 99 309.9951 17.26996 12.336746 23.735054
## 100 309.9951 17.26996 12.336746 23.735054
## 101 309.9951 17.26996 12.336746 23.735054
## 102 309.9951 17.26996 12.336746 23.735054
## 103 309.9951 17.26996 12.336746 23.735054
## 104 309.9951 17.26996 12.336746 23.735054
## 105 309.9951 17.26996 12.336746 23.735054
## 106 309.9951 17.26996 12.336746 23.735054
## 107 309.9951 17.26996 12.336746 23.735054
## 108 309.9951 17.26996 12.336746 23.735054
## 109 309.9951 17.26996 12.336746 23.735054
## 110 309.9951 17.26996 12.336746 23.735054
## 111 309.9951 17.26996 12.336746 23.735054
## 112 309.9951 17.26996 12.336746 23.735054
## 113 309.9951 17.26996 12.336746 23.735054
## 114 309.9951 17.26996 12.336746 23.735054
## 115 309.9951 17.26996 12.336746 23.735054
## 116 309.9951 17.26996 12.336746 23.735054
## 117 309.9951 17.26996 12.336746 23.735054
## 118 309.9951 17.26996 12.336746 23.735054
## 119 303.4624 31.24462 17.068977 46.124585
## 120 303.4624 31.24462 17.068977 46.124585
## 121 303.4624 31.24462 17.068977 46.124585
## 122 303.4624 31.24462 17.068977 46.124585
## 123 303.4624 31.24462 17.068977 46.124585
## 124 303.4624 31.24462 17.068977 46.124585
## 125 303.4624 31.24462 17.068977 46.124585
## 126 303.4624 31.24462 17.068977 46.124585
## 127 303.4624 31.24462 17.068977 46.124585
## 128 303.4624 31.24462 17.068977 46.124585
## 129 303.4624 31.24462 17.068977 46.124585
## 130 303.4624 31.24462 17.068977 46.124585
## 131 303.4624 31.24462 17.068977 46.124585
## 132 303.4624 31.24462 17.068977 46.124585
## 133 303.4624 31.24462 17.068977 46.124585
## 134 303.4624 31.24462 17.068977 46.124585
## 135 303.4624 31.24462 17.068977 46.124585
## 136 303.4624 31.24462 17.068977 46.124585
## 137 303.4624 31.24462 17.068977 46.124585
## 138 303.4624 31.24462 17.068977 46.124585
## 139 303.4624 31.24462 17.068977 46.124585
## 140 303.4624 31.24462 17.068977 46.124585
## 141 303.4624 31.24462 17.068977 46.124585
## 142 303.4624 31.24462 17.068977 46.124585
## 143 303.4624 31.24462 17.068977 46.124585
## 144 303.4624 31.24462 17.068977 46.124585
## 145 303.4624 31.24462 17.068977 46.124585
## 146 303.4624 31.24462 17.068977 46.124585
## 147 308.3743 29.94020 19.149973 39.827723
## 148 308.3743 29.94020 19.149973 39.827723
## 149 308.3743 29.94020 19.149973 39.827723
## 150 308.3743 29.94020 19.149973 39.827723
## 151 308.3743 29.94020 19.149973 39.827723
## 152 308.3743 29.94020 19.149973 39.827723
## 153 308.3743 29.94020 19.149973 39.827723
## 154 308.3743 29.94020 19.149973 39.827723
## 155 308.3743 29.94020 19.149973 39.827723
## 156 305.7809 28.92146 12.610394 52.003002
## 157 308.9513 26.00150 15.294786 43.304529
## 158 308.9513 26.00150 15.294786 43.304529
## 159 308.9513 26.00150 15.294786 43.304529
## 160 308.9513 26.00150 15.294786 43.304529
## 161 308.9513 26.00150 15.294786 43.304529
## 162 308.9513 26.00150 15.294786 43.304529
## 163 308.9513 26.00150 15.294786 43.304529
## 164 308.9513 26.00150 15.294786 43.304529
## 165 308.9513 26.00150 15.294786 43.304529
## 166 308.9513 26.00150 15.294786 43.304529
## 167 308.9513 26.00150 15.294786 43.304529
## 168 308.9513 26.00150 15.294786 43.304529
## 169 308.9513 26.00150 15.294786 43.304529
## 170 308.9513 26.00150 15.294786 43.304529
## 171 308.9513 26.00150 15.294786 43.304529
## 172 308.9513 26.00150 15.294786 43.304529
## 173 308.9513 26.00150 15.294786 43.304529
## 174 308.9513 26.00150 15.294786 43.304529
## 175 308.9513 26.00150 15.294786 43.304529
## 176 308.9513 26.00150 15.294786 43.304529
## 177 306.7128 26.35338 11.574460 44.970896
## 178 306.7128 26.35338 11.574460 44.970896
## 179 306.7128 26.35338 11.574460 44.970896
## 180 306.7128 26.35338 11.574460 44.970896
## 181 306.7128 26.35338 11.574460 44.970896
## 182 306.7128 26.35338 11.574460 44.970896
## 183 306.7128 26.35338 11.574460 44.970896
## 184 306.7128 26.35338 11.574460 44.970896
## 185 306.7128 26.35338 11.574460 44.970896
## 186 306.7128 26.35338 11.574460 44.970896
## 187 306.7128 26.35338 11.574460 44.970896
## 188 306.7128 26.35338 11.574460 44.970896
## 189 306.7128 26.35338 11.574460 44.970896
## 190 306.7128 26.35338 11.574460 44.970896
## 191 306.7128 26.35338 11.574460 44.970896
## 192 306.7128 26.35338 11.574460 44.970896
## 193 306.7128 26.35338 11.574460 44.970896
## 194 308.8187 23.49941 11.268511 32.454477
## 195 308.8187 23.49941 11.268511 32.454477
## 196 308.8187 23.49941 11.268511 32.454477
## 197 308.8187 23.49941 11.268511 32.454477
## 198 308.8187 23.49941 11.268511 32.454477
## 199 308.8187 23.49941 11.268511 32.454477
## 200 308.8187 23.49941 11.268511 32.454477
## 201 308.8187 23.49941 11.268511 32.454477
## 202 308.8187 23.49941 11.268511 32.454477
## 203 308.8187 23.49941 11.268511 32.454477
## 204 308.8187 23.49941 11.268511 32.454477
## 205 308.8187 23.49941 11.268511 32.454477
## 206 308.8187 23.49941 11.268511 32.454477
## 207 308.8187 23.49941 11.268511 32.454477
## 208 308.8187 23.49941 11.268511 32.454477
## 209 308.8187 23.49941 11.268511 32.454477
## 210 308.8187 23.49941 11.268511 32.454477
## 211 308.8187 23.49941 11.268511 32.454477
## 212 308.8187 23.49941 11.268511 32.454477
## 213 308.8187 23.49941 11.268511 32.454477
## 214 308.8187 23.49941 11.268511 32.454477
## 215 308.8187 23.49941 11.268511 32.454477
## 216 308.8187 23.49941 11.268511 32.454477
## 217 308.8187 23.49941 11.268511 32.454477
## 218 308.8187 23.49941 11.268511 32.454477
## 219 308.8187 23.49941 11.268511 32.454477
## 220 308.8187 23.49941 11.268511 32.454477
## 221 308.8187 23.49941 11.268511 32.454477
## 222 308.8187 23.49941 11.268511 32.454477
## 223 308.8187 23.49941 11.268511 32.454477
## 224 308.8187 23.49941 11.268511 32.454477
## 225 308.8187 23.49941 11.268511 32.454477
## 226 308.8187 23.49941 11.268511 32.454477
## 227 308.8187 23.49941 11.268511 32.454477
## 228 308.8187 23.49941 11.268511 32.454477
## 229 308.8187 23.49941 11.268511 32.454477
## 230 308.8187 23.49941 11.268511 32.454477
## 231 309.0836 16.22724 18.504738 47.899204
## 232 309.0836 16.22724 18.504738 47.899204
## 233 311.3618 23.94960 13.586511 43.712734
## 234 311.3618 23.94960 13.586511 43.712734
## 235 311.3618 23.94960 13.586511 43.712734
## 236 311.3618 23.94960 13.586511 43.712734
## 237 311.3618 23.94960 13.586511 43.712734
## 238 311.3618 23.94960 13.586511 43.712734
## 239 311.3618 23.94960 13.586511 43.712734
## 240 313.9219 27.37161 17.828087 38.702329
## 241 313.9219 27.37161 17.828087 38.702329
## 242 313.9219 27.37161 17.828087 38.702329
## 243 313.9219 27.37161 17.828087 38.702329
## 244 313.9219 27.37161 17.828087 38.702329
## 245 313.9219 27.37161 17.828087 38.702329
## 246 313.9219 27.37161 17.828087 38.702329
## 247 313.9219 27.37161 17.828087 38.702329
## 248 313.9219 27.37161 17.828087 38.702329
## 249 313.9219 27.37161 17.828087 38.702329
## 250 313.9219 27.37161 17.828087 38.702329
## 251 313.9219 27.37161 17.828087 38.702329
## 252 313.9219 27.37161 17.828087 38.702329
## 253 313.9219 27.37161 17.828087 38.702329
## 254 313.9219 27.37161 17.828087 38.702329
## 255 313.9219 27.37161 17.828087 38.702329
## 256 313.9219 27.37161 17.828087 38.702329
## 257 313.9219 27.37161 17.828087 38.702329
## 258 313.9219 27.37161 17.828087 38.702329
## 259 313.9219 27.37161 17.828087 38.702329
## 260 313.9219 27.37161 17.828087 38.702329
## 261 313.9219 27.37161 17.828087 38.702329
## 262 308.6350 36.02527 15.669551 50.737971
## 263 308.6350 36.02527 15.669551 50.737971
## 264 308.6350 36.02527 15.669551 50.737971
## 265 308.6350 36.02527 15.669551 50.737971
## 266 308.6350 36.02527 15.669551 50.737971
## 267 308.6350 36.02527 15.669551 50.737971
## 268 308.6350 36.02527 15.669551 50.737971
## 269 308.6350 36.02527 15.669551 50.737971
## 270 308.6350 36.02527 15.669551 50.737971
## 271 308.6350 36.02527 15.669551 50.737971
## 272 308.6350 36.02527 15.669551 50.737971
## 273 308.6350 36.02527 15.669551 50.737971
## 274 308.6350 36.02527 15.669551 50.737971
## 275 308.6350 36.02527 15.669551 50.737971
## 276 308.6350 36.02527 15.669551 50.737971
## 277 308.6350 36.02527 15.669551 50.737971
## 278 308.6350 36.02527 15.669551 50.737971
## 279 308.6350 36.02527 15.669551 50.737971
## 280 308.6350 36.02527 15.669551 50.737971
## 281 308.6350 36.02527 15.669551 50.737971
## 282 308.6350 36.02527 15.669551 50.737971
## 283 308.6350 36.02527 15.669551 50.737971
## 284 308.6350 36.02527 15.669551 50.737971
## 285 308.6350 36.02527 15.669551 50.737971
## 286 308.6350 36.02527 15.669551 50.737971
## 287 308.6350 36.02527 15.669551 50.737971
## 288 308.6350 36.02527 15.669551 50.737971
## 289 308.6350 36.02527 15.669551 50.737971
## 290 308.6350 36.02527 15.669551 50.737971
## 291 308.6350 36.02527 15.669551 50.737971
## 292 308.6350 36.02527 15.669551 50.737971
## 293 308.6350 36.02527 15.669551 50.737971
## 294 308.6350 36.02527 15.669551 50.737971
## 295 308.6350 36.02527 15.669551 50.737971
## 296 308.6350 36.02527 15.669551 50.737971
## 297 308.6350 36.02527 15.669551 50.737971
## 298 308.6350 36.02527 15.669551 50.737971
## 299 308.6350 36.02527 15.669551 50.737971
## 300 308.6350 36.02527 15.669551 50.737971
## 301 308.6350 36.02527 15.669551 50.737971
## 302 308.6350 36.02527 15.669551 50.737971
## 303 308.6350 36.02527 15.669551 50.737971
## 304 308.6350 36.02527 15.669551 50.737971
## 305 308.6350 36.02527 15.669551 50.737971
## 306 308.6350 36.02527 15.669551 50.737971
## 307 308.6350 36.02527 15.669551 50.737971
## 308 308.6350 36.02527 15.669551 50.737971
## 309 308.6350 36.02527 15.669551 50.737971
## 310 308.6350 36.02527 15.669551 50.737971
## 311 308.6350 36.02527 15.669551 50.737971
## 312 308.6350 36.02527 15.669551 50.737971
## 313 308.6350 36.02527 15.669551 50.737971
## 314 308.6350 36.02527 15.669551 50.737971
## 315 308.6350 36.02527 15.669551 50.737971
## 316 308.6350 36.02527 15.669551 50.737971
## 317 308.6350 36.02527 15.669551 50.737971
## 318 308.6350 36.02527 15.669551 50.737971
## 319 308.6350 36.02527 15.669551 50.737971
## 320 308.6350 36.02527 15.669551 50.737971
## 321 308.6350 36.02527 15.669551 50.737971
## 322 308.6350 36.02527 15.669551 50.737971
## 323 308.6350 36.02527 15.669551 50.737971
## 324 308.6350 36.02527 15.669551 50.737971
## 325 308.6350 36.02527 15.669551 50.737971
## 326 308.6350 36.02527 15.669551 50.737971
## 327 308.6350 36.02527 15.669551 50.737971
## 328 308.6350 36.02527 15.669551 50.737971
## 329 308.6350 36.02527 15.669551 50.737971
## 330 308.6350 36.02527 15.669551 50.737971
## 331 308.6350 36.02527 15.669551 50.737971
## 332 308.6350 36.02527 15.669551 50.737971
## 333 308.6350 36.02527 15.669551 50.737971
## 334 308.6350 36.02527 15.669551 50.737971
## 335 308.6350 36.02527 15.669551 50.737971
## 336 308.6350 36.02527 15.669551 50.737971
## 337 308.6350 36.02527 15.669551 50.737971
## 338 308.6350 36.02527 15.669551 50.737971
## 339 308.6350 36.02527 15.669551 50.737971
## 340 308.6350 36.02527 15.669551 50.737971
## 341 308.6350 36.02527 15.669551 50.737971
## 342 308.6350 36.02527 15.669551 50.737971
## 343 308.6350 36.02527 15.669551 50.737971
## 344 308.6350 36.02527 15.669551 50.737971
## 345 308.6350 36.02527 15.669551 50.737971
## 346 308.6350 36.02527 15.669551 50.737971
## 347 308.6350 36.02527 15.669551 50.737971
## 348 308.6350 36.02527 15.669551 50.737971
## 349 308.6350 36.02527 15.669551 50.737971
## 350 308.6350 36.02527 15.669551 50.737971
## 351 308.6350 36.02527 15.669551 50.737971
## 352 308.6350 36.02527 15.669551 50.737971
## 353 308.6350 36.02527 15.669551 50.737971
## 354 308.6350 36.02527 15.669551 50.737971
## 355 308.6350 36.02527 15.669551 50.737971
## 356 308.6350 36.02527 15.669551 50.737971
## 357 308.6350 36.02527 15.669551 50.737971
## 358 308.6350 36.02527 15.669551 50.737971
## 359 308.6350 36.02527 15.669551 50.737971
## 360 308.6350 36.02527 15.669551 50.737971
## 361 308.6350 36.02527 15.669551 50.737971
## 362 308.6350 36.02527 15.669551 50.737971
## 363 308.6350 36.02527 15.669551 50.737971
## 364 308.6350 36.02527 15.669551 50.737971
## 365 308.6350 36.02527 15.669551 50.737971
## 366 308.6350 36.02527 15.669551 50.737971
## 367 308.6350 36.02527 15.669551 50.737971
## 368 308.6350 36.02527 15.669551 50.737971
## 369 308.6350 36.02527 15.669551 50.737971
## 370 308.6350 36.02527 15.669551 50.737971
## 371 308.6350 36.02527 15.669551 50.737971
## 372 308.6350 36.02527 15.669551 50.737971
## 373 308.6350 36.02527 15.669551 50.737971
## 374 308.6350 36.02527 15.669551 50.737971
## 375 308.6350 36.02527 15.669551 50.737971
## 376 308.6350 36.02527 15.669551 50.737971
## 377 308.6350 36.02527 15.669551 50.737971
## 378 308.6350 36.02527 15.669551 50.737971
## 379 308.6350 36.02527 15.669551 50.737971
## 380 308.6350 36.02527 15.669551 50.737971
## 381 308.6350 36.02527 15.669551 50.737971
## 382 308.6350 36.02527 15.669551 50.737971
## 383 308.6350 36.02527 15.669551 50.737971
## 384 308.6350 36.02527 15.669551 50.737971
## 385 308.6350 36.02527 15.669551 50.737971
## 386 308.6350 36.02527 15.669551 50.737971
## 387 308.6350 36.02527 15.669551 50.737971
## 388 308.6350 36.02527 15.669551 50.737971
## 389 308.6350 36.02527 15.669551 50.737971
## 390 308.6350 36.02527 15.669551 50.737971
## 391 308.6350 36.02527 15.669551 50.737971
## 392 308.6350 36.02527 15.669551 50.737971
## 393 308.6350 36.02527 15.669551 50.737971
## 394 308.6350 36.02527 15.669551 50.737971
## 395 308.6350 36.02527 15.669551 50.737971
## 396 308.6350 36.02527 15.669551 50.737971
## 397 308.6350 36.02527 15.669551 50.737971
## 398 308.6350 36.02527 15.669551 50.737971
## 399 308.6350 36.02527 15.669551 50.737971
## 400 308.6350 36.02527 15.669551 50.737971
## 401 308.6350 36.02527 15.669551 50.737971
## 402 308.6350 36.02527 15.669551 50.737971
## 403 308.6350 36.02527 15.669551 50.737971
## 404 308.6350 36.02527 15.669551 50.737971
## 405 308.6350 36.02527 15.669551 50.737971
## 406 308.6350 36.02527 15.669551 50.737971
## 407 308.6350 36.02527 15.669551 50.737971
## 408 308.6350 36.02527 15.669551 50.737971
## 409 308.6350 36.02527 15.669551 50.737971
## 410 308.6350 36.02527 15.669551 50.737971
## 411 308.6350 36.02527 15.669551 50.737971
## 412 308.6350 36.02527 15.669551 50.737971
## 413 308.6350 36.02527 15.669551 50.737971
## 414 308.6350 36.02527 15.669551 50.737971
## 415 308.6350 36.02527 15.669551 50.737971
## 416 308.6350 36.02527 15.669551 50.737971
## 417 308.6350 36.02527 15.669551 50.737971
## 418 308.6350 36.02527 15.669551 50.737971
## 419 308.6350 36.02527 15.669551 50.737971
## 420 308.6350 36.02527 15.669551 50.737971
## 421 308.6350 36.02527 15.669551 50.737971
## 422 308.6350 36.02527 15.669551 50.737971
## 423 308.6350 36.02527 15.669551 50.737971
## 424 308.6350 36.02527 15.669551 50.737971
## 425 308.6350 36.02527 15.669551 50.737971
## 426 308.6350 36.02527 15.669551 50.737971
## 427 308.6350 36.02527 15.669551 50.737971
## 428 308.6350 36.02527 15.669551 50.737971
## 429 308.6350 36.02527 15.669551 50.737971
## 430 308.6350 36.02527 15.669551 50.737971
## 431 308.6350 36.02527 15.669551 50.737971
## 432 308.6350 36.02527 15.669551 50.737971
## 433 308.6350 36.02527 15.669551 50.737971
## 434 308.6350 36.02527 15.669551 50.737971
## 435 308.6350 36.02527 15.669551 50.737971
## 436 308.6350 36.02527 15.669551 50.737971
## 437 308.6350 36.02527 15.669551 50.737971
## 438 308.6350 36.02527 15.669551 50.737971
## 439 308.6350 36.02527 15.669551 50.737971
## 440 308.6350 36.02527 15.669551 50.737971
## 441 308.6350 36.02527 15.669551 50.737971
## 442 308.6350 36.02527 15.669551 50.737971
## 443 308.6350 36.02527 15.669551 50.737971
## 444 308.6350 36.02527 15.669551 50.737971
## 445 308.6350 36.02527 15.669551 50.737971
## 446 308.6350 36.02527 15.669551 50.737971
## 447 308.6350 36.02527 15.669551 50.737971
## 448 308.6350 36.02527 15.669551 50.737971
## 449 308.6350 36.02527 15.669551 50.737971
## 450 308.6350 36.02527 15.669551 50.737971
## 451 308.6350 36.02527 15.669551 50.737971
## 452 308.6350 36.02527 15.669551 50.737971
## 453 308.6350 36.02527 15.669551 50.737971
## 454 308.6350 36.02527 15.669551 50.737971
## 455 308.6350 36.02527 15.669551 50.737971
## 456 308.6350 36.02527 15.669551 50.737971
## 457 308.6350 36.02527 15.669551 50.737971
## 458 308.6350 36.02527 15.669551 50.737971
## 459 308.6350 36.02527 15.669551 50.737971
## 460 308.6350 36.02527 15.669551 50.737971
## 461 308.6350 36.02527 15.669551 50.737971
## 462 308.6350 36.02527 15.669551 50.737971
## 463 308.6350 36.02527 15.669551 50.737971
## 464 308.6350 36.02527 15.669551 50.737971
## 465 308.6350 36.02527 15.669551 50.737971
## 466 308.6350 36.02527 15.669551 50.737971
## 467 308.6350 36.02527 15.669551 50.737971
## 468 308.6350 36.02527 15.669551 50.737971
## 469 308.6350 36.02527 15.669551 50.737971
## 470 308.6350 36.02527 15.669551 50.737971
## 471 308.6350 36.02527 15.669551 50.737971
## 472 308.6350 36.02527 15.669551 50.737971
## 473 308.6350 36.02527 15.669551 50.737971
## 474 308.6350 36.02527 15.669551 50.737971
## 475 308.6350 36.02527 15.669551 50.737971
## 476 308.6350 36.02527 15.669551 50.737971
## 477 308.6350 36.02527 15.669551 50.737971
## 478 308.6350 36.02527 15.669551 50.737971
## 479 308.6350 36.02527 15.669551 50.737971
## 480 308.6350 36.02527 15.669551 50.737971
## 481 308.6350 36.02527 15.669551 50.737971
## 482 308.6350 36.02527 15.669551 50.737971
## 483 308.6350 36.02527 15.669551 50.737971
## 484 308.6350 36.02527 15.669551 50.737971
## 485 308.6350 36.02527 15.669551 50.737971
## 486 308.6350 36.02527 15.669551 50.737971
## 487 308.6350 36.02527 15.669551 50.737971
## 488 308.6350 36.02527 15.669551 50.737971
## 489 308.6350 36.02527 15.669551 50.737971
## 490 308.6350 36.02527 15.669551 50.737971
## 491 308.6350 36.02527 15.669551 50.737971
## 492 308.6350 36.02527 15.669551 50.737971
## 493 308.6350 36.02527 15.669551 50.737971
## 494 308.6350 36.02527 15.669551 50.737971
## 495 308.6350 36.02527 15.669551 50.737971
## 496 306.9936 31.66718 20.847377 41.561076
## 497 306.9936 31.66718 20.847377 41.561076
## 498 306.9936 31.66718 20.847377 41.561076
## 499 306.9936 31.66718 20.847377 41.561076
## 500 306.9936 31.66718 20.847377 41.561076
## 501 306.9936 31.66718 20.847377 41.561076
## 502 306.9936 31.66718 20.847377 41.561076
## 503 306.9936 31.66718 20.847377 41.561076
## 504 306.9936 31.66718 20.847377 41.561076
## 505 306.9936 31.66718 20.847377 41.561076
## 506 306.9936 31.66718 20.847377 41.561076
## 507 306.9936 31.66718 20.847377 41.561076
## 508 306.9936 31.66718 20.847377 41.561076
## 509 306.9936 31.66718 20.847377 41.561076
## 510 306.9936 31.66718 20.847377 41.561076
## 511 306.9936 31.66718 20.847377 41.561076
## 512 306.9936 31.66718 20.847377 41.561076
## 513 306.9936 31.66718 20.847377 41.561076
## 514 306.9936 31.66718 20.847377 41.561076
## 515 306.9936 31.66718 20.847377 41.561076
## 516 306.9936 31.66718 20.847377 41.561076
## 517 306.9936 31.66718 20.847377 41.561076
## 518 306.9936 31.66718 20.847377 41.561076
## 519 306.9936 31.66718 20.847377 41.561076
## 520 306.9936 31.66718 20.847377 41.561076
## 521 306.9936 31.66718 20.847377 41.561076
## 522 306.9936 31.66718 20.847377 41.561076
## 523 306.9936 31.66718 20.847377 41.561076
## 524 306.9936 31.66718 20.847377 41.561076
## 525 306.9936 31.66718 20.847377 41.561076
## 526 306.9936 31.66718 20.847377 41.561076
## 527 306.9936 31.66718 20.847377 41.561076
## 528 306.9936 31.66718 20.847377 41.561076
## 529 306.9936 31.66718 20.847377 41.561076
## 530 306.9936 31.66718 20.847377 41.561076
## 531 306.9936 31.66718 20.847377 41.561076
## 532 306.9936 31.66718 20.847377 41.561076
## 533 306.9936 31.66718 20.847377 41.561076
## 534 306.9936 31.66718 20.847377 41.561076
## 535 306.9936 31.66718 20.847377 41.561076
## 536 306.9936 31.66718 20.847377 41.561076
## 537 306.9936 31.66718 20.847377 41.561076
## 538 306.9936 31.66718 20.847377 41.561076
## 539 306.9936 31.66718 20.847377 41.561076
## 540 306.9936 31.66718 20.847377 41.561076
## 541 306.9936 31.66718 20.847377 41.561076
## 542 306.9936 31.66718 20.847377 41.561076
## 543 306.9936 31.66718 20.847377 41.561076
## 544 306.9936 31.66718 20.847377 41.561076
## 545 306.9936 31.66718 20.847377 41.561076
## 546 306.9936 31.66718 20.847377 41.561076
## 547 306.9936 31.66718 20.847377 41.561076
## 548 306.9936 31.66718 20.847377 41.561076
## 549 306.9936 31.66718 20.847377 41.561076
## 550 306.9936 31.66718 20.847377 41.561076
## 551 306.9936 31.66718 20.847377 41.561076
## 552 306.9936 31.66718 20.847377 41.561076
## 553 306.9936 31.66718 20.847377 41.561076
## 554 306.9936 31.66718 20.847377 41.561076
## 555 306.9936 31.66718 20.847377 41.561076
## 556 306.9936 31.66718 20.847377 41.561076
## 557 306.9936 31.66718 20.847377 41.561076
## 558 306.9936 31.66718 20.847377 41.561076
## 559 306.9936 31.66718 20.847377 41.561076
## 560 306.9936 31.66718 20.847377 41.561076
## 561 306.9936 31.66718 20.847377 41.561076
## 562 306.9936 31.66718 20.847377 41.561076
## 563 306.9936 31.66718 20.847377 41.561076
## 564 306.9936 31.66718 20.847377 41.561076
## 565 306.9936 31.66718 20.847377 41.561076
## 566 306.9936 31.66718 20.847377 41.561076
## 567 306.9936 31.66718 20.847377 41.561076
## 568 306.9936 31.66718 20.847377 41.561076
## 569 306.9936 31.66718 20.847377 41.561076
## 570 306.9936 31.66718 20.847377 41.561076
## 571 306.9936 31.66718 20.847377 41.561076
## 572 306.9936 31.66718 20.847377 41.561076
## 573 306.9936 31.66718 20.847377 41.561076
## 574 306.9936 31.66718 20.847377 41.561076
## 575 306.9936 31.66718 20.847377 41.561076
## 576 306.9936 31.66718 20.847377 41.561076
## 577 306.9936 31.66718 20.847377 41.561076
## 578 306.9936 31.66718 20.847377 41.561076
## 579 306.9936 31.66718 20.847377 41.561076
## 580 306.9936 31.66718 20.847377 41.561076
## 581 306.9936 31.66718 20.847377 41.561076
## 582 306.9936 31.66718 20.847377 41.561076
## 583 306.9936 31.66718 20.847377 41.561076
## 584 306.9936 31.66718 20.847377 41.561076
## 585 306.9936 31.66718 20.847377 41.561076
## 586 306.9936 31.66718 20.847377 41.561076
## 587 306.9936 31.66718 20.847377 41.561076
## 588 306.9936 31.66718 20.847377 41.561076
## 589 306.9936 31.66718 20.847377 41.561076
## 590 306.9936 31.66718 20.847377 41.561076
## 591 306.9936 31.66718 20.847377 41.561076
## 592 306.9936 31.66718 20.847377 41.561076
## 593 306.9936 31.66718 20.847377 41.561076
## 594 306.9936 31.66718 20.847377 41.561076
## 595 306.9936 31.66718 20.847377 41.561076
## 596 306.9936 31.66718 20.847377 41.561076
## 597 306.9936 31.66718 20.847377 41.561076
## 598 306.9936 31.66718 20.847377 41.561076
## 599 306.9936 31.66718 20.847377 41.561076
## 600 306.9936 31.66718 20.847377 41.561076
## 601 306.9936 31.66718 20.847377 41.561076
## 602 306.9936 31.66718 20.847377 41.561076
## 603 306.9936 31.66718 20.847377 41.561076
## 604 306.9936 31.66718 20.847377 41.561076
## 605 306.9936 31.66718 20.847377 41.561076
## 606 306.9936 31.66718 20.847377 41.561076
## 607 302.4901 24.94336 17.638276 44.454310
## 608 302.4901 24.94336 17.638276 44.454310
## 609 302.4901 24.94336 17.638276 44.454310
## 610 307.2487 22.22715 14.368510 33.439437
## 611 307.2487 22.22715 14.368510 33.439437
## 612 307.2487 22.22715 14.368510 33.439437
## 613 307.2487 22.22715 14.368510 33.439437
## 614 307.2487 22.22715 14.368510 33.439437
## 615 307.2487 22.22715 14.368510 33.439437
## 616 307.2487 22.22715 14.368510 33.439437
## 617 307.2487 22.22715 14.368510 33.439437
## 618 307.2487 22.22715 14.368510 33.439437
## 619 307.2487 22.22715 14.368510 33.439437
## 620 307.2487 22.22715 14.368510 33.439437
## 621 307.2487 22.22715 14.368510 33.439437
## 622 307.2487 22.22715 14.368510 33.439437
## 623 307.2487 22.22715 14.368510 33.439437
## 624 307.2487 22.22715 14.368510 33.439437
## 625 307.2487 22.22715 14.368510 33.439437
## 626 307.2487 22.22715 14.368510 33.439437
## 627 307.2487 22.22715 14.368510 33.439437
## 628 307.2487 22.22715 14.368510 33.439437
## 629 307.2487 22.22715 14.368510 33.439437
## 630 307.2487 22.22715 14.368510 33.439437
## 631 307.2487 22.22715 14.368510 33.439437
## 632 299.8966 16.71972 20.447530 32.258431
## 633 299.8966 16.71972 20.447530 32.258431
## 634 299.8966 16.71972 20.447530 32.258431
## 635 299.8966 16.71972 20.447530 32.258431
## 636 299.8966 16.71972 20.447530 32.258431
## 637 299.8966 16.71972 20.447530 32.258431
## 638 299.8966 16.71972 20.447530 32.258431
## 639 299.8966 16.71972 20.447530 32.258431
## 640 299.8966 16.71972 20.447530 32.258431
## 641 299.8966 16.71972 20.447530 32.258431
## 642 299.8966 16.71972 20.447530 32.258431
## 643 299.8966 16.71972 20.447530 32.258431
## 644 299.8966 16.71972 20.447530 32.258431
## 645 299.8966 16.71972 20.447530 32.258431
## 646 299.8966 16.71972 20.447530 32.258431
## 647 299.8966 16.71972 20.447530 32.258431
## 648 299.8966 16.71972 20.447530 32.258431
## 649 299.8966 16.71972 20.447530 32.258431
## 650 299.8966 16.71972 20.447530 32.258431
## 651 299.8966 16.71972 20.447530 32.258431
## 652 299.8966 16.71972 20.447530 32.258431
## 653 299.8966 16.71972 20.447530 32.258431
## 654 299.8966 16.71972 20.447530 32.258431
## 655 299.8966 16.71972 20.447530 32.258431
## 656 299.8966 16.71972 20.447530 32.258431
## 657 299.8966 16.71972 20.447530 32.258431
## 658 299.8966 16.71972 20.447530 32.258431
## 659 299.8966 16.71972 20.447530 32.258431
## 660 299.8966 16.71972 20.447530 32.258431
## 661 299.8966 16.71972 20.447530 32.258431
## 662 299.8966 16.71972 20.447530 32.258431
## 663 299.8966 16.71972 20.447530 32.258431
## 664 299.8966 16.71972 20.447530 32.258431
## 665 299.8966 16.71972 20.447530 32.258431
## 666 299.8966 16.71972 20.447530 32.258431
## 667 299.8966 16.71972 20.447530 32.258431
## 668 299.8966 16.71972 20.447530 32.258431
## 669 299.8966 16.71972 20.447530 32.258431
## 670 299.8966 16.71972 20.447530 32.258431
## 671 299.8966 16.71972 20.447530 32.258431
## 672 299.8966 16.71972 20.447530 32.258431
## 673 299.8966 16.71972 20.447530 32.258431
## 674 299.8966 16.71972 20.447530 32.258431
## 675 299.8966 16.71972 20.447530 32.258431
## 676 299.8966 16.71972 20.447530 32.258431
## 677 299.8966 16.71972 20.447530 32.258431
## 678 299.8966 16.71972 20.447530 32.258431
## 679 299.8966 16.71972 20.447530 32.258431
## 680 299.8966 16.71972 20.447530 32.258431
## 681 299.8966 16.71972 20.447530 32.258431
## 682 299.8966 16.71972 20.447530 32.258431
## 683 299.8966 16.71972 20.447530 32.258431
## 684 299.8966 16.71972 20.447530 32.258431
## 685 299.8966 16.71972 20.447530 32.258431
## 686 299.8966 16.71972 20.447530 32.258431
## 687 299.8966 16.71972 20.447530 32.258431
## 688 299.8966 16.71972 20.447530 32.258431
## 689 299.8966 16.71972 20.447530 32.258431
## 690 299.8966 16.71972 20.447530 32.258431
## 691 299.8966 16.71972 20.447530 32.258431
## 692 299.8966 16.71972 20.447530 32.258431
## 693 299.8966 16.71972 20.447530 32.258431
## 694 299.8966 16.71972 20.447530 32.258431
## 695 299.8966 16.71972 20.447530 32.258431
## 696 299.8966 16.71972 20.447530 32.258431
## 697 299.8966 16.71972 20.447530 32.258431
## 698 306.4289 16.12922 17.631279 31.783014
## 699 306.4289 16.12922 17.631279 31.783014
## 700 306.4289 16.12922 17.631279 31.783014
## 701 306.4289 16.12922 17.631279 31.783014
## 702 306.4289 16.12922 17.631279 31.783014
## 703 306.4289 16.12922 17.631279 31.783014
## 704 306.4289 16.12922 17.631279 31.783014
## 705 306.4289 16.12922 17.631279 31.783014
## 706 306.4289 16.12922 17.631279 31.783014
## 707 306.4289 16.12922 17.631279 31.783014
## 708 306.4289 16.12922 17.631279 31.783014
## 709 306.4289 16.12922 17.631279 31.783014
## 710 306.4289 16.12922 17.631279 31.783014
## 711 306.4289 16.12922 17.631279 31.783014
## 712 306.4289 16.12922 17.631279 31.783014
## 713 306.4289 16.12922 17.631279 31.783014
## 714 302.7856 18.00522 17.522833 34.176029
## 715 302.7856 18.00522 17.522833 34.176029
## 716 302.7856 18.00522 17.522833 34.176029
## 717 302.7856 18.00522 17.522833 34.176029
## 718 302.7856 18.00522 17.522833 34.176029
## 719 302.7856 18.00522 17.522833 34.176029
## 720 302.7856 18.00522 17.522833 34.176029
## 721 302.7856 18.00522 17.522833 34.176029
## 722 302.7856 18.00522 17.522833 34.176029
## 723 302.7856 18.00522 17.522833 34.176029
## 724 302.7856 18.00522 17.522833 34.176029
## 725 302.7856 18.00522 17.522833 34.176029
## 726 302.7856 18.00522 17.522833 34.176029
## 727 302.7856 18.00522 17.522833 34.176029
## 728 302.7856 18.00522 17.522833 34.176029
## 729 302.7856 18.00522 17.522833 34.176029
## 730 302.7856 18.00522 17.522833 34.176029
## 731 302.7856 18.00522 17.522833 34.176029
## 732 302.7856 18.00522 17.522833 34.176029
## 733 302.7856 18.00522 17.522833 34.176029
## 734 305.2558 17.08801 10.728323 39.386198
## 735 305.2558 17.08801 10.728323 39.386198
## 736 305.2558 17.08801 10.728323 39.386198
## 737 305.2558 17.08801 10.728323 39.386198
## 738 305.2558 17.08801 10.728323 39.386198
## 739 305.2558 17.08801 10.728323 39.386198
## 740 305.2558 17.08801 10.728323 39.386198
## 741 305.2558 17.08801 10.728323 39.386198
## 742 305.2558 17.08801 10.728323 39.386198
## 743 305.2558 17.08801 10.728323 39.386198
## 744 305.2558 17.08801 10.728323 39.386198
## 745 305.2558 17.08801 10.728323 39.386198
## 746 305.2558 17.08801 10.728323 39.386198
## 747 305.2558 17.08801 10.728323 39.386198
## 748 305.2558 17.08801 10.728323 39.386198
## 749 305.2558 17.08801 10.728323 39.386198
## 750 305.2558 17.08801 10.728323 39.386198
## 751 305.2558 17.08801 10.728323 39.386198
## 752 305.2558 17.08801 10.728323 39.386198
## 753 305.2558 17.08801 10.728323 39.386198
## 754 305.2558 17.08801 10.728323 39.386198
## 755 305.2558 17.08801 10.728323 39.386198
## 756 305.2558 17.08801 10.728323 39.386198
## 757 305.2558 17.08801 10.728323 39.386198
## 758 305.2558 17.08801 10.728323 39.386198
## 759 305.2558 17.08801 10.728323 39.386198
## 760 305.2558 17.08801 10.728323 39.386198
## 761 305.2558 17.08801 10.728323 39.386198
## 762 305.2558 17.08801 10.728323 39.386198
## 763 305.2558 17.08801 10.728323 39.386198
## 764 305.2558 17.08801 10.728323 39.386198
## 765 305.2558 17.08801 10.728323 39.386198
## 766 305.2558 17.08801 10.728323 39.386198
## 767 305.2558 17.08801 10.728323 39.386198
## 768 303.9617 24.17268 16.473057 33.444963
## 769 303.9617 24.17268 16.473057 33.444963
## 770 303.9617 24.17268 16.473057 33.444963
## 771 303.9617 24.17268 16.473057 33.444963
## 772 303.9617 24.17268 16.473057 33.444963
## 773 303.9617 24.17268 16.473057 33.444963
## 774 303.9617 24.17268 16.473057 33.444963
## 775 303.9617 24.17268 16.473057 33.444963
## 776 303.9617 24.17268 16.473057 33.444963
## 777 303.9617 24.17268 16.473057 33.444963
## 778 303.9617 24.17268 16.473057 33.444963
## 779 303.9617 24.17268 16.473057 33.444963
## 780 303.9617 24.17268 16.473057 33.444963
## 781 303.9617 24.17268 16.473057 33.444963
## 782 303.9617 24.17268 16.473057 33.444963
## 783 307.5307 21.04660 10.218341 23.547714
## 784 307.5307 21.04660 10.218341 23.547714
## 785 307.5307 21.04660 10.218341 23.547714
## 786 307.5307 21.04660 10.218341 23.547714
## 787 307.5307 21.04660 10.218341 23.547714
## 788 307.5307 21.04660 10.218341 23.547714
## 789 307.5307 21.04660 10.218341 23.547714
## 790 307.5307 21.04660 10.218341 23.547714
## 791 307.5307 21.04660 10.218341 23.547714
## 792 307.5307 21.04660 10.218341 23.547714
## 793 307.5307 21.04660 10.218341 23.547714
## 794 307.5307 21.04660 10.218341 23.547714
## 795 307.5307 21.04660 10.218341 23.547714
## 796 307.5307 21.04660 10.218341 23.547714
## 797 307.5307 21.04660 10.218341 23.547714
## 798 307.5307 21.04660 10.218341 23.547714
## 799 307.5307 21.04660 10.218341 23.547714
## 800 307.5307 21.04660 10.218341 23.547714
## 801 307.5307 21.04660 10.218341 23.547714
## 802 307.5307 21.04660 10.218341 23.547714
## 803 307.5307 21.04660 10.218341 23.547714
## 804 307.5307 21.04660 10.218341 23.547714
## 805 307.5307 21.04660 10.218341 23.547714
## 806 307.5307 21.04660 10.218341 23.547714
## 807 307.5307 21.04660 10.218341 23.547714
## 808 307.5307 21.04660 10.218341 23.547714
## 809 307.5307 21.04660 10.218341 23.547714
## 810 307.5307 21.04660 10.218341 23.547714
## 811 307.5307 21.04660 10.218341 23.547714
## 812 307.5307 21.04660 10.218341 23.547714
## 813 307.5307 21.04660 10.218341 23.547714
## 814 307.5307 21.04660 10.218341 23.547714
## 815 307.5307 21.04660 10.218341 23.547714
## 816 307.5307 21.04660 10.218341 23.547714
## 817 307.5307 21.04660 10.218341 23.547714
## 818 307.5307 21.04660 10.218341 23.547714
## 819 307.5307 21.04660 10.218341 23.547714
## 820 307.5307 21.04660 10.218341 23.547714
## 821 307.5307 21.04660 10.218341 23.547714
## 822 307.5307 21.04660 10.218341 23.547714
## 823 307.5307 21.04660 10.218341 23.547714
## 824 307.5307 21.04660 10.218341 23.547714
## 825 307.5307 21.04660 10.218341 23.547714
## 826 307.5307 21.04660 10.218341 23.547714
## 827 307.5307 21.04660 10.218341 23.547714
## 828 307.5307 21.04660 10.218341 23.547714
## 829 307.5307 21.04660 10.218341 23.547714
## 830 307.5307 21.04660 10.218341 23.547714
## 831 307.5307 21.04660 10.218341 23.547714
## 832 307.5307 21.04660 10.218341 23.547714
## 833 307.5307 21.04660 10.218341 23.547714
## 834 307.5307 21.04660 10.218341 23.547714
## 835 307.5307 21.04660 10.218341 23.547714
## 836 307.5307 21.04660 10.218341 23.547714
## 837 307.5307 21.04660 10.218341 23.547714
## 838 307.5307 21.04660 10.218341 23.547714
## 839 307.5307 21.04660 10.218341 23.547714
## 840 307.5307 21.04660 10.218341 23.547714
## 841 307.5307 21.04660 10.218341 23.547714
## 842 307.5307 21.04660 10.218341 23.547714
## 843 307.5307 21.04660 10.218341 23.547714
## 844 307.5307 21.04660 10.218341 23.547714
## 845 307.5307 21.04660 10.218341 23.547714
## 846 307.5307 21.04660 10.218341 23.547714
## 847 307.5307 21.04660 10.218341 23.547714
## 848 307.5307 21.04660 10.218341 23.547714
## 849 307.5307 21.04660 10.218341 23.547714
## 850 307.5307 21.04660 10.218341 23.547714
## 851 307.5307 21.04660 10.218341 23.547714
## 852 307.5307 21.04660 10.218341 23.547714
## 853 307.5307 21.04660 10.218341 23.547714
## 854 307.5307 21.04660 10.218341 23.547714
## 855 307.5307 21.04660 10.218341 23.547714
## 856 307.5307 21.04660 10.218341 23.547714
## 857 307.5307 21.04660 10.218341 23.547714
## 858 307.5307 21.04660 10.218341 23.547714
## 859 307.5307 21.04660 10.218341 23.547714
## 860 307.5307 21.04660 10.218341 23.547714
## 861 307.5307 21.04660 10.218341 23.547714
## 862 307.5307 21.04660 10.218341 23.547714
## 863 307.5307 21.04660 10.218341 23.547714
## 864 307.5307 21.04660 10.218341 23.547714
## 865 307.5307 21.04660 10.218341 23.547714
## 866 307.5307 21.04660 10.218341 23.547714
## 867 307.5307 21.04660 10.218341 23.547714
## 868 307.5307 21.04660 10.218341 23.547714
## 869 307.5307 21.04660 10.218341 23.547714
## 870 307.5307 21.04660 10.218341 23.547714
## 871 307.5307 21.04660 10.218341 23.547714
## 872 307.5307 21.04660 10.218341 23.547714
## 873 307.5307 21.04660 10.218341 23.547714
## 874 307.5307 21.04660 10.218341 23.547714
## 875 307.5307 21.04660 10.218341 23.547714
## 876 307.5307 21.04660 10.218341 23.547714
## 877 307.5307 21.04660 10.218341 23.547714
## 878 307.5307 21.04660 10.218341 23.547714
## 879 307.5307 21.04660 10.218341 23.547714
## 880 307.5307 21.04660 10.218341 23.547714
## 881 307.5307 21.04660 10.218341 23.547714
## 882 307.5307 21.04660 10.218341 23.547714
## 883 307.5307 21.04660 10.218341 23.547714
## 884 307.5307 21.04660 10.218341 23.547714
## 885 307.5307 21.04660 10.218341 23.547714
## 886 307.5307 21.04660 10.218341 23.547714
## 887 307.5307 21.04660 10.218341 23.547714
## 888 307.5307 21.04660 10.218341 23.547714
## 889 307.5307 21.04660 10.218341 23.547714
## 890 307.5307 21.04660 10.218341 23.547714
## 891 307.5307 21.04660 10.218341 23.547714
## 892 307.5307 21.04660 10.218341 23.547714
## 893 307.5307 21.04660 10.218341 23.547714
## 894 307.5307 21.04660 10.218341 23.547714
## 895 307.5307 21.04660 10.218341 23.547714
## 896 307.5307 21.04660 10.218341 23.547714
## 897 307.5307 21.04660 10.218341 23.547714
## 898 307.5307 21.04660 10.218341 23.547714
## 899 307.5307 21.04660 10.218341 23.547714
## 900 307.5307 21.04660 10.218341 23.547714
## 901 304.5009 29.40649 9.287965 39.486832
## 902 304.5009 29.40649 9.287965 39.486832
## 903 304.5009 29.40649 9.287965 39.486832
## 904 304.5009 29.40649 9.287965 39.486832
## 905 304.5009 29.40649 9.287965 39.486832
## 906 304.5009 29.40649 9.287965 39.486832
## 907 304.5009 29.40649 9.287965 39.486832
## 908 304.5009 29.40649 9.287965 39.486832
## 909 304.5009 29.40649 9.287965 39.486832
## 910 304.5009 29.40649 9.287965 39.486832
## 911 304.5009 29.40649 9.287965 39.486832
## 912 304.5009 29.40649 9.287965 39.486832
## 913 304.5009 29.40649 9.287965 39.486832
## 914 304.5009 29.40649 9.287965 39.486832
## 915 304.5009 29.40649 9.287965 39.486832
## 916 304.5009 29.40649 9.287965 39.486832
## 917 304.5009 29.40649 9.287965 39.486832
## 918 304.5009 29.40649 9.287965 39.486832
## 919 304.5009 29.40649 9.287965 39.486832
## 920 304.5009 29.40649 9.287965 39.486832
## 921 304.5009 29.40649 9.287965 39.486832
## 922 304.5009 29.40649 9.287965 39.486832
## 923 304.5009 29.40649 9.287965 39.486832
## 924 304.5009 29.40649 9.287965 39.486832
## 925 299.3447 24.85734 19.575741 28.919504
## 926 299.3447 24.85734 19.575741 28.919504
## 927 299.3447 24.85734 19.575741 28.919504
## 928 299.3447 24.85734 19.575741 28.919504
## 929 299.3447 24.85734 19.575741 28.919504
## 930 299.3447 24.85734 19.575741 28.919504
## 931 299.3447 24.85734 19.575741 28.919504
## 932 299.3447 24.85734 19.575741 28.919504
## 933 299.3447 24.85734 19.575741 28.919504
## 934 299.3447 24.85734 19.575741 28.919504
## 935 299.3447 24.85734 19.575741 28.919504
## 936 299.3447 24.85734 19.575741 28.919504
## 937 299.3447 24.85734 19.575741 28.919504
## 938 299.3447 24.85734 19.575741 28.919504
## 939 299.3447 24.85734 19.575741 28.919504
## 940 299.3447 24.85734 19.575741 28.919504
## 941 299.3447 24.85734 19.575741 28.919504
## 942 299.3447 24.85734 19.575741 28.919504
## 943 299.3447 24.85734 19.575741 28.919504
## 944 299.3447 24.85734 19.575741 28.919504
## 945 299.3447 24.85734 19.575741 28.919504
## 946 299.3447 24.85734 19.575741 28.919504
## 947 299.3447 24.85734 19.575741 28.919504
## 948 299.3447 24.85734 19.575741 28.919504
## 949 299.3447 24.85734 19.575741 28.919504
## 950 299.3447 24.85734 19.575741 28.919504
## 951 299.3447 24.85734 19.575741 28.919504
## 952 299.3447 24.85734 19.575741 28.919504
## 953 299.3447 24.85734 19.575741 28.919504
## 954 299.3447 24.85734 19.575741 28.919504
## 955 299.3447 24.85734 19.575741 28.919504
## 956 299.3447 24.85734 19.575741 28.919504
## 957 299.3447 24.85734 19.575741 28.919504
## 958 299.3447 24.85734 19.575741 28.919504
## 959 299.3447 24.85734 19.575741 28.919504
## 960 299.3447 24.85734 19.575741 28.919504
## 961 299.3447 24.85734 19.575741 28.919504
## 962 299.3447 24.85734 19.575741 28.919504
## 963 299.3447 24.85734 19.575741 28.919504
## 964 299.3447 24.85734 19.575741 28.919504
## 965 299.3447 24.85734 19.575741 28.919504
## 966 299.3447 24.85734 19.575741 28.919504
## 967 299.3447 24.85734 19.575741 28.919504
## 968 299.3447 24.85734 19.575741 28.919504
## 969 299.3447 24.85734 19.575741 28.919504
## 970 299.3447 24.85734 19.575741 28.919504
## 971 299.3447 24.85734 19.575741 28.919504
## 972 299.3447 24.85734 19.575741 28.919504
## 973 299.3447 24.85734 19.575741 28.919504
## 974 299.3447 24.85734 19.575741 28.919504
## 975 299.3447 24.85734 19.575741 28.919504
## 976 299.3447 24.85734 19.575741 28.919504
## 977 299.3447 24.85734 19.575741 28.919504
## 978 299.3447 24.85734 19.575741 28.919504
## 979 299.3447 24.85734 19.575741 28.919504
## 980 299.3447 24.85734 19.575741 28.919504
## 981 299.3447 24.85734 19.575741 28.919504
## 982 299.3447 24.85734 19.575741 28.919504
## 983 299.3447 24.85734 19.575741 28.919504
## 984 299.3447 24.85734 19.575741 28.919504
## 985 299.3447 24.85734 19.575741 28.919504
## 986 299.3447 24.85734 19.575741 28.919504
## 987 299.3447 24.85734 19.575741 28.919504
## 988 299.3447 24.85734 19.575741 28.919504
## 989 305.2199 19.63974 18.880842 44.353577
## 990 305.2199 19.63974 18.880842 44.353577
## 991 305.2199 19.63974 18.880842 44.353577
## 992 305.2199 19.63974 18.880842 44.353577
## 993 305.2199 19.63974 18.880842 44.353577
## 994 305.2199 19.63974 18.880842 44.353577
## 995 305.2199 19.63974 18.880842 44.353577
## 996 305.2199 19.63974 18.880842 44.353577
## 997 305.2199 19.63974 18.880842 44.353577
## 998 305.2199 19.63974 18.880842 44.353577
## 999 305.2199 19.63974 18.880842 44.353577
## 1000 305.2199 19.63974 18.880842 44.353577
## 1001 305.2199 19.63974 18.880842 44.353577
ggpairs(locs)
plot(locs$x, col="red")
lines(rep(states[[order(ls, decreasing = 1)[1]]]$points[[2]]$x, length(locs$x)))
plot(locs$y, col="green")
plot(locs$w, col="blue")
plot(locs$h, col="purple")